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Found 12 papers, 6 papers with code
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain.
Neural Characteristic Function Learning for Conditional Image Generation
The emergence of conditional generative adversarial networks (cGANs) has revolutionised the way we approach and control the generation, by means of adversarially learning joint distributions of data and auxiliary information.
Demystifying CNNs for Images by Matched Filters
The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era.
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method
E-commerce images are playing a central role in attracting people's attention when retailing and shopping online, and an accurate attention prediction is of significant importance for both customers and retailers, where its research is yet to start.
Ranked #2 on
Saliency Prediction
on SALECI
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video
When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i. e., a progressive quality assessment paradigm.
Von Mises-Fisher Elliptical Distribution
A large class of modern probabilistic learning systems assumes symmetric distributions, however, real-world data tend to obey skewed distributions and are thus not always adequately modelled through symmetric distributions.
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
In this paper, we propose a novel solution for non-convex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of sub-problems corresponding to each variable, and then iteratively optimize each sub-problem using a fixed updating rule.
Reciprocal Adversarial Learning via Characteristic Functions
For rigour, we first establish the physical meaning of the phase and amplitude in CF, and show that this provides a feasible way of balancing the accuracy and diversity of generation.
Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution.
Solving general elliptical mixture models through an approximate Wasserstein manifold
To relieve this issue, we introduce an efficient optimisation method on a statistical manifold defined under an approximate Wasserstein distance, which allows for explicit metrics and computable operations, thus significantly stabilising and improving the EMM estimation.
Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models
To resolve this issue, we design a new cost function, which is capable of controlling the balance between the phase and the amplitude contribution to the solution.
A universal framework for learning the elliptical mixture model
Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM).
